Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Khuzani, Masoud Badiei"'
We develop and analyze a projected particle Langevin optimization method to learn the distribution in the Sch\"{o}nberg integral representation of the radial basis functions from training samples. More specifically, we characterize a distributionally
Externí odkaz:
http://arxiv.org/abs/2006.13330
Autor:
Seo, Hyunseok, Khuzani, Masoud Badiei, Vasudevan, Varun, Huang, Charles, Ren, Hongyi, Xiao, Ruoxiu, Jia, Xiao, Xing, Lei
In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning alg
Externí odkaz:
http://arxiv.org/abs/1911.02521
We propose a novel supervised learning method to optimize the kernel in the maximum mean discrepancy generative adversarial networks (MMD GANs), and the kernel support vector machines (SVMs). Specifically, we characterize a distributionally robust op
Externí odkaz:
http://arxiv.org/abs/1909.11820
We study the problem of learning policy of an infinite-horizon, discounted cost, Markov decision process (MDP) with a large number of states. We compute the actions of a policy that is nearly as good as a policy chosen by a suitable oracle from a giv
Externí odkaz:
http://arxiv.org/abs/1903.06727
We propose a novel data-driven method to learn a mixture of multiple kernels with random features that is certifiabaly robust against adverserial inputs. Specifically, we consider a distributionally robust optimization of the kernel-target alignment
Externí odkaz:
http://arxiv.org/abs/1902.10365
Autor:
Khuzani, Masoud Badiei, Li, Na
We study a stochastic primal-dual method for constrained optimization over Riemannian manifolds with bounded sectional curvature. We prove non-asymptotic convergence to the optimal objective value. More precisely, for the class of hyperbolic manifold
Externí odkaz:
http://arxiv.org/abs/1703.08167
Autor:
Khuzani, Masoud Badiei, Li, Na
We study deterministic and stochastic primal-dual sub-gradient algorithms for distributed optimization of a separable objective function with global inequality constraints. In both algorithms, the norm of the Lagrangian multipliers are controlled by
Externí odkaz:
http://arxiv.org/abs/1609.08262
We study the problem of lossy joint source-channel coding in a single-user energy harvesting communication system with causal energy arrivals and the energy storage unit may have leakage. In particular, we investigate the achievable distortion in the
Externí odkaz:
http://arxiv.org/abs/1508.04526
We study the transmission of a set of correlated sources $(U_1,\cdots,U_K)$ over a Gaussian multiple access relay channel with time asynchronism between the encoders. We assume that the maximum possible offset ${\mathsf{d_{max}}}(n)$ between the tran
Externí odkaz:
http://arxiv.org/abs/1408.1750
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